What Is Q-learning?

Seems something that would pop up in a James bond movie with all the drama that comes with it. The Q department has created something extraordinary for James bond to learn from. Well, it is not that dramatic, but it is so helpful that even he wouldn't mind using it. Q-learning is the name of a learning algorithm that uses mathematical models to evaluate policy choices in the context of a Markov decision process. It is similar to other reinforcement learning algorithms but has unique characteristics that make it stand out from the crowd. The technical makeup of Q-learning includes an agent, a set of states and a set of actions per state. The Q function uses weights for various steps and a discount factor to value rewards. Q-learning is one of the essential algorithms in the world of artificial intelligence, machine learning and other related fields. It's a model-free reinforcement learning algorithm that uses stochastic modeling to find the best path forward for an agent in a Markov decision process (MDP). The technical makeup of Q-learning includes an agent, a set of states, and a bunch of actions per state. The Q function uses weights for various steps and a discount factor in evaluating rewards. Although it may seem like a simple idea, Q-learning is critical in many types of reinforcement learning and deep learning models. One of the best examples is where deep Q-learning is used to help machine learning programs learn gameplay strategies in various video games, such as Atari games from the 1980s. Here a convolutional neural network takes samples of gameplay recordings to work up a stochastic model that will help the computer learn how to play better over time. Q-learning has excellent potential to help advance artificial intelligence and machine learning techniques across many different industries!